This research examined what mix of building types result in the most efficient use of a technology known as Aquifer Thermal Energy Storage (ATES). Hourly energy simulation models for six different building archetypes were created based on representative building characteristic and energy use data from the Toronto area. A genetic algorithm optimization tool was then created to vary scheduling and production properties of the ATES system and the relative number of different building archetypes. The tool found that a cooling season from weeks 16‐42 maximized the useful energy output of the ATES and resulted in roughly 30% reduction in heating and cooling energy use and associated GHG emissions. It was also found that creating a mix consisting of a higher percentage of larger buildings than is currently found in most neighbourhoods could reduce energy usage by an additional 10%.

This research examined what mix of building types result in the most efficient use of a technology known as Aquifer Thermal Energy Storage (ATES). Hourly energy simulation models for six different building archetypes were created based on representative building characteristic and energy use data from the Toronto area. A genetic algorithm optimization tool was then created to vary scheduling and production properties of the ATES system and the relative number of different building archetypes. The tool found that a cooling season from weeks 16‐42 maximized the useful energy output of the ATES and resulted in roughly 30% reduction in heating and cooling energy use and associated GHG emissions. It was also found that creating a mix consisting of a higher percentage of larger buildings than is currently found in most neighbourhoods could reduce energy usage by an additional 10%.